What Determines Thalamic Spatio-Temporal Properties?

描述(申请人提供)：丘脑神经元如何整合其不同的前馈和反馈输入？哺乳动物外侧膝状核(LGN)中的大多数输入和突触是在视网膜外的，但这些不同的输入是如何整合以控制视觉信息从视网膜到皮质的流动的方式尚不清楚。特别是，皮质和束旁核(PGN)的下行输入对LGN中继神经元感受野的时空特性的影响尚不清楚，尽管这些输入的大小和复杂性强烈表明它们的重要性，但如果没有它们，LGN可能是不必要的。为了解决这一认识缺口，我们将把生理实验和计算建模相结合，以达到下列目标：1)测量来自V1的下行反馈通路(可逆)失活之前和期间猫LGN感受场的时空结构；2)比较有V1反馈和没有V1反馈的LGN神经元在视网膜中的空间总和；3)测量V1层6层神经元的时间传递函数；4)构建包含V1和PGN下行通路的LGN中继神经元的计算模型；5)对照生理测量验证模型的预测。所提出的模型建立在我们的初始(静态)反馈模型的基础上，将采用种群动力学的创新模拟方法，并将是对皮质丘脑反馈及其动力学进行建模的首批尝试之一。它将在神经科学中罕见的规模上使用并行计算：两台强大的计算机集群，以及一台非常大的IBM超级计算机，它可以容纳比过去可能尝试的更大、更复杂的模型。这些结果将促进我们对LGN下行输入在建立其敏感性、动力学、接受场结构和放电模式中所起的作用的理解，并将为我们未来扩展我们的早期视觉系统的进化模型提供必要的垫脚石。健康相关性：更完整地了解LGN如何结合其不同的输入，特别是它的时间行为如何依赖于大脑皮质，应该会导致对它在动态大脑疾病(如癫痫)中的作用的深入了解。更广泛地说，了解反馈电路的作用将有助于我们理解其他动态病理，如帕金森氏症。

DESCRIPTION (provided by applicant): How do thalamic neurons integrate their various feedforward and feedback inputs? Most of the inputs and synapses in the mammalian lateral geniculate nucleus (LGN) are extra-retinal, but the way in which these diverse inputs are integrated to control the flow of visual information from retina to cortex is not understood. In particular, the influence of the descending inputs from the cortex and the perigeniculate nucleus (PGN) on the spatiotemporal properties of receptive fields of LGN relay neurons is unknown, although the size and complexity of these inputs strongly suggest their importance, without them, the LGN might arguably be unnecessary. To address this knowledge gap, we shall combine physiological experiments with computational modeling to achieve the following aims: 1) Measure the spatiotemporal structure of receptive fields in the cat LGN before and during (reversible) inactivation of the descending feedback pathway from V1; 2) Compare spatial summation in the retina with that of LGN neurons with and without V1 feedback; 3) Measure the temporal transfer function of neurons in layer 6 of V1; 4) Construct computational models of LGN relay neurons that incorporate the descending pathway from V1 and the PGN, and 5) Validate the models' predictions against physiological measurements. The proposed modeling, which builds on our initial (static) feedback model, will employ the innovative simulation approach of population kinetics, and will be one of the first attempts to model the corticothalamic feedback and its dynamics. It will use parallel computation on a scale not commonly found in neuroscience: two clusters of powerful computers, and a very large IBM supercomputer, which can accommodate larger, more complex models than could have been attempted in the past. The results will advance our understanding of the role that the descending inputs to the LGN play in establishing its sensitivity, dynamics, receptive field structure and discharge pattern, and will provide a necessary stepping stone for future expansions of our evolving model of the early visual system. HEALTH RELEVANCE: A more complete knowledge of how the LGN combines its diverse inputs, and especially how its temporal behavior depends on the cortex, should lead to insights into its role in dynamical brain diseases, such as epilepsy. More generally, an understanding of the role of feedback circuits will help us understand other dynamical pathologies, such as Parkinson's disease.